Data privacy and fairness in recommender systems
In early 2020, IKEA made a promise to give back the control to customers over their data. In this talk, we look at two aspects of the promise in the context of data-driven product recommendations. First, we analyse performance of such systems under minimal data requirements. Second, we propose a Bayesian approach to recommendations that uses in-session active learning to give personalised content without collection of private data.
Martin is a data science researcher at IKEA group. He works with data-powered AI to enrich the customer experience at every point of the customer journey. In his current research, he develops algorithms that provide personalised content while respecting fair use of the customer's data. Prior to joining IKEA, he was a researcher in the machine learning group at the University of Oxford.
